Cross-tabulation with crosstab() in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ data_output

intermediate

Output of simple crosstab() with two categorical columns

Given the DataFrame below, what is the output of the crosstab() function?

Data Analysis Python

import pandas as pd

data = {'Gender': ['Male', 'Female', 'Female', 'Male', 'Female'],
        'Preference': ['Coffee', 'Coffee', 'Tea', 'Coffee', 'Tea']}
df = pd.DataFrame(data)

result = pd.crosstab(df['Gender'], df['Preference'])
print(result)

A

Preference  Coffee  Tea
Gender                 
Female          1    2
Male            2    0

B

Preference  Coffee  Tea
Gender                 
Female          2    1
Male            1    2

C

Preference  Coffee  Tea
Gender                 
Female          1    3
Male            2    0

D

Preference  Coffee  Tea
Gender                 
Female          0    2
Male            2    1

💡 Hint

Count how many times each Gender prefers Coffee or Tea.

The crosstab counts occurrences of each Gender-Preference pair. Female prefers Coffee once and Tea twice. Male prefers Coffee twice and Tea zero times.

🧠 Conceptual

intermediate

Understanding margins parameter in crosstab()

What does setting the parameter margins=true do in the pd.crosstab() function?

A Normalizes the crosstab values to show proportions instead of counts.

B Adds a row and column with totals (sum) for each category and overall.

C Filters the crosstab to only show rows with more than 5 counts.

D Sorts the crosstab rows and columns alphabetically.

💡 Hint

Think about what 'margins' means in tables.

The margins=true option adds an extra row and column showing the total counts for each row and column, plus the grand total.

🔧 Debug

advanced

Identify the error in crosstab() usage

What error will this code raise?

Data Analysis Python

import pandas as pd

ages = [23, 45, 31, 35]
genders = ['M', 'F', 'F', 'M']

# Valid: passing lists to crosstab
result = pd.crosstab(ages, genders, margins=True)
print(result)

A No error, prints the crosstab table

B TypeError: unhashable type: 'list'

C TypeError: Cannot interpret 'ages' as a data frame column

D ValueError: Index contains duplicate entries, cannot reshape

💡 Hint

pd.crosstab accepts lists as array-like inputs.

Passing lists directly to pd.crosstab() is valid as they are array-like. margins=True adds the margins successfully. No error is raised.

❓ visualization

advanced

Visualizing crosstab output with a heatmap

Which code snippet correctly creates a heatmap visualization of the crosstab result using seaborn?

Data Analysis Python

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

data = {'City': ['NY', 'LA', 'NY', 'LA', 'NY'],
        'Transport': ['Car', 'Bus', 'Bus', 'Car', 'Car']}
df = pd.DataFrame(data)

ct = pd.crosstab(df['City'], df['Transport'])

A

plt.plot(ct)
plt.show()

B

sns.barplot(data=ct)
plt.show()

C

sns.heatmap(ct, annot=True)
plt.show()

D

sns.scatterplot(data=ct)
plt.show()

💡 Hint

Heatmaps are good for showing tables with numbers.

sns.heatmap() is designed to visualize matrix-like data such as crosstab outputs. annot=True shows the numbers on the heatmap.

🚀 Application

expert

Using crosstab() to analyze survey data with normalization

You have a DataFrame with columns 'AgeGroup' and 'Satisfaction' from a survey. You want to see the proportion of each satisfaction level within each age group (rows sum to 1). Which crosstab() call achieves this?

Data Analysis Python

import pandas as pd

data = {'AgeGroup': ['18-25', '18-25', '26-35', '26-35', '26-35', '36-45'],
        'Satisfaction': ['High', 'Low', 'Medium', 'High', 'Low', 'High']}
df = pd.DataFrame(data)

A pd.crosstab(df['AgeGroup'], df['Satisfaction'], margins=True, normalize='all')

B pd.crosstab(df['AgeGroup'], df['Satisfaction'], normalize='columns')

C pd.crosstab(df['AgeGroup'], df['Satisfaction'], normalize=True)

D pd.crosstab(df['AgeGroup'], df['Satisfaction'], normalize='index')

💡 Hint

Normalization by 'index' means row-wise proportions.

normalize='index' divides each row by its total, showing proportions within each age group.